By counting the number of observations that exist for each of the 60 unique combination of variables, QCA can determine which descriptive inferences or implications are empirically supported by a data set.
[4] The method is used in social science and is based on the binary logic of Boolean algebra, and attempts to ensure that all possible combinations of variables that can be made across the cases under investigation are considered.
[5] This technique allows the identification of multiple causal pathways and interaction effects that may not be detectable via statistical analysis that typically requires its data set to conform to one model.
[6] Statistical methodologists have argued that QCA's strong assumptions render its findings both fragile and prone to type I error.
Simon Hug argues that deterministic hypotheses and error-free measures are exceedingly rare in social science and uses Monte Carlo simulations to demonstrate the fragility of QCA results if either assumption is violated.
[7] Chris Krogslund, Donghyun Danny Choi, and Mathias Poertner further demonstrate that QCA results are highly sensitive to minor parametric and model-susceptibility changes and are vulnerable to type I error.
[9] Braumoeller also offers a formal test of the null hypothesis and demonstrates that even very convincing QCA findings may be the result of chance.
In real-life complex societal processes, QCA enables the identification of multiple sets of conditions that are consistently associated with a particular output value in order to explore for causal predictors.